Bibliographic record
Abstract
Since the introduction of glyphosate resistant (GR) soybean cultivars in 1996, GR crop have become the most rapidly and globally accepted agronomic practice in the history of agriculture (Anonymous 2007; Service 2007). In the United States (US), more than one billion cumulative acres of genetically engineered (GE) crops have been planted with most of these represented by GR crops (Gianessi 2005; Anonymous 2007; Service 2007; Gianessi 2008). Monsanto reported that their 2006 market share of glyphosate resistant crops (GRC) included 71.6 million acres of the soybean area, 34 million acres of corn, 11 .3 million acres of cotton, and 5.7 million acres of canola in the US and Canada (Anonymous 2006b). They anticipate that their GRC market share in US corn will approach 60 million acres by 2010 (Anonymous 2006a). In Iowa, GR soybeans are planted on an estimated 97% of the acres and GR corn is approaching 75% of the acres. All of the GR crops likely receive at least one and more often, two or more applications of glyphosate for weed control. Growers like the perception of simplicity and convenience of the GR-based crop systems, the consistent control of weeds and the lack of crop injury from glyphosate. However, despite the appearance of a successful weed control program, the soybean producer often fails to recognize the cost of simplicity and convenience: given the widespread adoption of GR crop systems, weed populations are changing and there is a critical need to include glyphosate stewardship in GR crop systems. The concurrent use of glyphosate in GRCs has resulted in the evolution of weeds that are resistant to glyphosate (Figure 1). There are now 14 different weed species that have glyphosate resistant biotypes (Heap 2004). Nine glyphosate resistant weed species have been identified in the US and six were confirmed glyphosate resistant since 2004. Importantly, it is apparent that weed populations are evolving resistance to glyphosate at an increasing rate. While alternative herbicides may still be effective and provide control of the glyphosate resistant weeds, resistance to many of the alternative herbicides also exists. Currently 183 herbicide resistant weed species are identified and many of these weeds are found in Iowa (Heap 2004). Thus it is critical to understand the implications of current weed control practices on future weed problems and assess the need to implement stewardship proactively to protect the sustainability of GRCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".